Novel effects of rosiglitazone on SMAD2 and SMAD3 signaling in white adipose tissue of diabetic rats
Bibliographic record
Abstract
OBJECTIVE: The effects of the proliferator-activated receptor gamma (PPARγ) agonist rosiglitazone (ROSI) on the transforming growth factor (TGF)-β/SMAD signaling pathway in white adipose tissue (WAT) of diabetic rats were assessed. METHODS: Six-week-old, male ZDF rats were fed a chow diet with (ZDF ROSI) or without (ZDF chow) ROSI (diet, 100 mg/kg) for 6 weeks. Subcutaneous (scWAT) and retroperitoneal (rpWAT) adipose tissues were excised to quantify the protein content/phosphorylation. RESULTS: ZDF ROSI animals showed enhanced glucose tolerance and mitochondrial protein content in both depots. The protein content of enzymes involved in fatty acid handling was increased in scWAT of ZDF ROSI animals. ZDF ROSI exhibited decreased phosphorylation of SMAD2 and SMAD3 exclusively in scWAT, along with increases in inhibitory SMAD7 and the E3 ubiquitin ligase SMURF2. In contrast, ROSI increased the protein content of SMAD4, TGF-β receptor I and II, and SMAD Anchor for Receptor Activation in scWAT. CONCLUSIONS: For the first time, the fact that ROSI inhibits SMAD2 and SMAD3 signaling in a depot-specific manner in diabetic rats was demonstrated. In scWAT, ROSI reduced SMAD2 and SMAD3 phosphorylation, likely through the inhibitory actions of SMAD7 and SMURF2. Induction of proximal components of the SMAD pathway may constitute a feedback mechanism to counteract ROSI-induced lipid synthesis in scWAT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".